Knowledge catalogue
arXiv:2603.07721v2 Announce Type: replace-cross Abstract: Brand advertising plays a critical role in building long-term consumer awareness and loyalty, making it a key objective for advertisers across
arXiv:2511.03962v2 Announce Type: replace Abstract: Accurate intrinsic calibration is a crucial yet challenging prerequisite for 3D reconstruction using light field cameras. Existing calibration model
arXiv:2606.22652v1 Announce Type: new Abstract: We propose Markov Chain from Human Feedback (MCHF), an elementary approach for aligning generative models from pairwise human preferences. Unlike Reinfo
arXiv:2606.22046v1 Announce Type: new Abstract: Tailless flapping-wing micro-aerial vehicles (FWMAVs) mimic the impressive flight performance of hummingbirds, utilising unsteady aerodynamic effects. H
arXiv:2606.20031v2 Announce Type: replace Abstract: Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS
arXiv:2606.21252v1 Announce Type: new Abstract: Video-based seizure detection is essential for the management of epilepsy patients, offering a non-invasive complement to electroencephalography. While
arXiv:2606.21639v1 Announce Type: new Abstract: Minimum Spanning Trees have been used in unsupervised learning, particularly in clustering tasks, due to their ability to recognize clusters by removing
arXiv:2606.23010v1 Announce Type: new Abstract: Predicting temporal Quality of Service (QoS) data is critical for optimizing network services and rationalizing resource allocation in cloud computing a
arXiv:2606.21771v1 Announce Type: new Abstract: Fish achieve efficient swimming across varied speeds through active modulation of their body flexibility. To explore the effects of tunable stiffness on
arXiv:2604.04098v2 Announce Type: replace Abstract: Precision livestock farming requires accurate and timely heat stress prediction to ensure animal welfare and optimize farm management. This study pr
arXiv:2407.07357v4 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing. While deep gra
arXiv:2606.20671v1 Announce Type: new Abstract: Neural wrappers are learned pre-and postprocessing networks designed to enhance the performance of conventional video codecs. Although these approaches
arXiv:2606.23658v1 Announce Type: new Abstract: Woven structures exhibit rich mechanical behaviors including anisotropic stiffness, shear-induced locking, and crimp interchange that emerge purely from
arXiv:2606.23355v1 Announce Type: new Abstract: Dynamic feedback linearization (DFL) is a classical technique for trajectory tracking of unicycle-type mobile robots, but the resulting DFL-based contro
arXiv:2606.21350v1 Announce Type: new Abstract: This paper introduces an interpretation of Temporal Behavior Trees (TBTs) as Reward-Petri-Nets (RPNs) for reinforcement learning (RL). Designing reward
arXiv:2603.13293v2 Announce Type: replace Abstract: Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models
arXiv:2606.21216v1 Announce Type: new Abstract: Trained policies for real-world robotics rely on computer vision components, typically in the form of pre-trained visual encoders. These encoders are an
arXiv:2606.21511v1 Announce Type: cross Abstract: Respiratory rate is a vital indicator of pulmonary and cardiovascular health, yet conventional methods for estimating respiratory rate are often intru
arXiv:2606.21568v1 Announce Type: new Abstract: Student behavior detection is important for intelligent classroom analysis but remains challenging in large-class scenarios due to dense instance co-occ
arXiv:2606.23572v1 Announce Type: new Abstract: We develop a spectral theory for normalized corrected GNN propagation. The object of study is the symmetric normalized adjacency with its degree-station
arXiv:2606.21960v1 Announce Type: new Abstract: Laser-induced breakdown spectroscopy (LIBS) faces challenges in high-accuracy quantitative measurement under few-shot scenarios due to spectral noise an
arXiv:2507.07907v2 Announce Type: replace-cross Abstract: Learning is a complex dynamical process shaped by a range of interconnected decisions. Careful design of hyperparameter schedules for artifici
arXiv:2606.21509v1 Announce Type: new Abstract: End-to-end autonomous driving systems tightly couple perception and decision-making through latent representations. Consequently, updates to perception
arXiv:2606.22360v1 Announce Type: new Abstract: Successful conversations require speakers to align on the meaning of concepts, a challenging but crucial task for human-robot interaction. Understanding
arXiv:2606.21304v1 Announce Type: new Abstract: This paper introduces the CERTH-ITI solution for the MediaEval NewsImages 2026 challenge, which focuses on generating images related to news headlines.
arXiv:2606.20681v1 Announce Type: new Abstract: Slope hazards constitute a major safety threat to expressway infrastructure, and their evolution is typically manifested as slow surface deformation. Co
arXiv:2606.21220v1 Announce Type: new Abstract: Inspection of offshore wind turbine rotor blades is critical for predictive maintenance to maximise efficiency and extend operational lifetime. However,
arXiv:2606.22881v1 Announce Type: new Abstract: LiDAR (Light Detection and Ranging) sensors capture the surrounding environment as dense 3D point clouds by measuring the time-of-flight of emitted lase
arXiv:2606.21884v1 Announce Type: new Abstract: It is tempting to assume any task solvable by a short program can be taught to a model as its chain-of-thought: write the steps out, fine-tune, and the
arXiv:2606.20620v1 Announce Type: new Abstract: Starting from the axiomatic formulation of scale-space theory, we develop a viscosity-solution framework for multiscale image representations arising fr
arXiv:2606.23574v1 Announce Type: cross Abstract: Vision-language-action (VLA) models and world-action models (WAM) are the generative models now driving general-purpose robot control, turning raw cam
arXiv:2606.21528v1 Announce Type: cross Abstract: We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the extragrad
arXiv:2606.22112v1 Announce Type: new Abstract: The performance of advanced materials for extreme environments is underpinned by their microstructure, including the size and distribution of reinforcin
arXiv:2606.21244v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves exceptional real-time rendering, but its substantial computational and storage demands hinder widespread deployment. Exis
arXiv:2606.22091v1 Announce Type: new Abstract: Per-scene 3D Gaussian Splatting (3DGS) enables high-fidelity rendering, but practical robotic and AR scene capture pipelines often depend on external ge
arXiv:2603.20819v2 Announce Type: replace Abstract: This paper studies finite-sample set-membership identification for discrete-time bilinear systems under bounded symmetric log-concave disturbances.
arXiv:2606.23662v1 Announce Type: cross Abstract: Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfa
arXiv:2503.01290v3 Announce Type: replace Abstract: Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challengi
arXiv:2602.02451v2 Announce Type: replace Abstract: Discovering causal relationships requires controlled experiments, but experimentalists face a sequential decision problem: each intervention reveals
arXiv:2606.22277v1 Announce Type: cross Abstract: We study trajectory optimization in mobile sensing systems that must identify which member of a finite candidate set is the true target, while maintai
arXiv:2602.14257v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains
arXiv:2606.22326v1 Announce Type: cross Abstract: Adam is one of the most widely implemented and influential modern optimizers. Why is it effective across different optimization problems in practice?
arXiv:2511.06675v2 Announce Type: replace-cross Abstract: Beside the standard stochastic gradient descent (SGD) method, the Adam optimizer due to Kingma & Ba (2014) is currently probably the best-know
arXiv:2606.21757v1 Announce Type: new Abstract: We present AdaPrivate-TS, a differentially private contextual bandit algorithm that combines Thompson Sampling with batched zCDP composition. Our key in
arXiv:2606.21713v1 Announce Type: cross Abstract: In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs
arXiv:2606.23359v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide an effective way to solve partial differential equations (PDEs) by embedding physical principles into t
arXiv:2606.22462v1 Announce Type: new Abstract: Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream
arXiv:2606.23079v1 Announce Type: new Abstract: Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but this incurs
arXiv:2606.19891v2 Announce Type: replace Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action
arXiv:2606.21736v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain a
arXiv:2606.20880v1 Announce Type: cross Abstract: Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertaint
arXiv:2606.20697v1 Announce Type: new Abstract: AlphaEarth Foundations (AEF) unify global remote sensing foundation embeddings through multimodal self-supervised learning, but their pretraining focuse
arXiv:2606.21013v1 Announce Type: cross Abstract: Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financ
arXiv:2506.04018v3 Announce Type: replace-cross Abstract: As Large Language Model (LLM) agents become more widespread, associated misalignment risks increase. While prior research has studied agents'
arXiv:2606.21892v1 Announce Type: cross Abstract: Crop recommendation systems in precision agriculture have long suffered from a fundamental modality gap: visual soil characterization and chemical nut
arXiv:2603.20179v3 Announce Type: replace-cross Abstract: Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipelin
arXiv:2503.00366v3 Announce Type: replace-cross Abstract: Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imba
arXiv:2606.23678v1 Announce Type: new Abstract: Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance multimodal large language models (MLLMs) has become a pi
arXiv:2410.07397v2 Announce Type: replace Abstract: As data-driven modeling of physical dynamical systems becomes more prevalent, a new challenge is emerging: making these models more compatible and a
arXiv:2606.22475v1 Announce Type: cross Abstract: Modern web applications increasingly combine three ingredients that are hard to test: output from large language models, multi-market internationaliza